The performance of our single model and the ensemble result on the validation set are shown in Table REF . We trained RTIC with variants of text encoders. The SWEM [1]} indicates that we encoded the sentence embedding as the concatenation of the average pool and the max pool of the word embedding by GloVe [2]} instead of encoding a sentence by LSTM or GRU from scratch. Although the performance of the individual model does not exceed our best single model (RITC, 38.22), the ensemble result is improved significantly (Ensemble 4 models, 45.05). With the iterative process of the ensemble using models trained in various experimental settings, the score is maximized to 47.55. At last, we merge 80% of validation pairs into train pairs and trained the additional models. The results of these models on the validation set are not compared in Table REF  since it could break the tendency between the test and the validation score. Finally, by adding the result of the additional models, the ensemble score on the test was marginally improved (46.16 \(\rightarrow \)  48.02).
